Fuzzy modelling and robust control with applications to robotic manipulators

摘要

In this thesis, fuzzy modelling of a class of nonlinear systems has been investigatedudbased on fuzzy logic and linear feedback control theory, and a few robust variableudstructure control schemes for nonlinear systems have been developed. A number ofudrobustness and convergence results with dramatically reduced control chattering areudpresented for variable structure control systems with applications to roboticudmanipulators in the presence of parameter variations and external disturbances. Theudmajor outcomes of the work described in this thesis are summarised as follows.udA robust tracking control scheme is proposed for a class of nonlinear systems withudfuzzy model. It is shown that a nominal system model for a nonlinear system isudestablished by fuzzy synthesis of a set of linearised local subsystems, where theudconventional linear feedback control technique is used to design a feedback controllerudfor the fuzzy nominal system. A variable structure compensator is then designed toudeliminate the effects of the approximation error and system uncertainties. Strongudrobustness with respect to large system uncertainties and asymptotic convergence ofudthe output tracking error are obtained.udA sliding mode control scheme using fuzzy logic and Lyapunov stability theory hasudbeen proposed. It is shown that a sliding mode is first designed to describe the desiredudsystem dynamics for the controlled system. A set of fuzzy rules are then used to adjustudthe controller's parameters based on the Lyapunov function and its time derivative.udThe desired system dynamics are then obtained in the sliding mode. The sliding modeudcontrollers with fuzzy tuning algorithm show the advantage of reducing the chatteringudof the control signals, compared with the conventional sliding mode controllers. A robust continuous sliding mode control scheme for linear systems with uncertaintiesudhas been presented. The controller consists of three components: equivalent control,udcontinuous reaching mode control and robust control. It retains the positive propertiesudof sliding mode control but without the disadvantage of control chattering. The proposed control scheme has been applied to the tracking control of a one-link roboticudmanipulator with fuzzy modelling of the nonlinear system.udA robust adaptive sliding mode control scheme with fuzzy tuning has been presented.udIt is shown that an adaptive sliding mode control is first designed to learn the systemudparameters with bounded system uncertainties and external disturbances. A set ofudfuzzy rules are then used to adjust the controller's uncertainty bound based on theudLyapunov function and its time derivative. The robust adaptive sliding modeudcontroller with fuzzy tuning algorithm show the advantage of reducing the chatteringudand the amplitude of the control signals, compared with the adaptive sliding modeudcontroller without fuzzy tuning. Experimental example for a five-bar robot arm isudgiven in support of the proposed control scheme.udFinally, a new adaptive sliding mode controller has been developed for trajectoryudtracking in robotic manipulators. This controller is able to estimate the constant partudof the system parameters as well as adaptively learn the uncertain part of the systemudparameters by the Gaussian neural network. It is shown that under a mild assumption,udthe proposed control law does not require measurement of acceleration signals. Thisudnew control law exhibits the good aspects of Slotine and Li's (1987) and keeps theudchattering to a minimum level. An experiment of a five bar robotic system was doneudand the results have confirmed the effectiveness of the approach.